Public sector-related data can be expansive, containing census data, property risk characteristics, historical loss information, risk rating matrices and natural hazard event scientific tracking. In order to facilitate packaging the sometimes unwieldy data in a way that is useful for risk decision making, utilizing outside resources to improve data transparency can be valuable. Public sector resources devoted to building tools that measure risks that are perceived as “uninsurable” can unlock private sector funding.
The private insurance sector also has detailed data on claims, premium patterns and rating trends. In addition to the data itself, the private (re)insurance sector has dedicated financial and intellectual resources that can develop and refine computer models to simulate various kinds of catastrophic losses, such as earthquake, tornado, terrorism, hurricane and flood. The private sector is also able to apply the simulations to portfolios of similar risks that have been combined in order to determine the amount of potential loss a specific community would experience in today’s dollars if faced with historical storms.
Regardless of the economy, developed or emerging, underlying risk data is the foundation for robust use of technology. The more detail collected for pertinent risk characteristics, the more precise the technical evaluation will be. It is important to work with an experienced analyst to identify critical risk characteristics that are necessary for the varying tools and regions. With proper data input, use of tools and technology is the foundation for effective risk management strategies.
Data and sophisticated analytic tools are essential for evaluating risk. Data and the tools provide consistent and validated assessment of risk that can be replicated by third parties during risk transfer negotiations. For the risk taker the ability to monitor exposure data and intersect it with big data that informs risk decisions is critical. Visualizing risk accumulations with mapping data, satellite imagery and surveillance drone output is critical for monitoring and managing risk accumulations. In addition to exposure management, catastrophe models are used to simulate events most likely to impact a portfolio of risks. Output can then be used to properly price risk and protect capital.
Financial models are then constructed to consider the cost of capital with and without risk transfer. These tools are essential in optimizing portfolios and capital for an entity - private or public.